Prostatic cancer is a disease characterized by marked heterogeneity of its morphology and its clinical behavior. Earlier detection has had an apparent impact on decreasing mortality and has spurred an increased interest in early detection. This seeming success has, however, raised a number of issues regarding the over-detection of small, low-grade carcinomas that may be clinically insignificant. This application represents a translational effort that uses computer modeling to investigate clinically relevant issues related to the biology of prostate cancer as they apply to the detection of prostate cancer and the prediction of its prognosis. The immediate intent is to improve our ability to detect the disease and at the same time, provide guidance regarding which patients should receive definitive forms of therapy. The proposal is focused around three Specific Aims. The first Aim is directed at the development of advanced strategies for prostate needle biopsy. Studies on modification of existing biopsy techniques based on the presence of an elevated PSA, abnormal DRE, or PIN, determination of sampling error, and correlation with body imaging are included. Clinical validation of computer simulated results is also proposed. The second Aim addresses the natural history of latent and small clinical carcinomas. Studies on the morphometric characteristics of latent carcinomas in various ethnic groups (North American Caucasians, African-Americans and Japanese men), tumor doubling times using BrdU incorporation, trends in carcinoma characteristics (volume and Gleason scores) over time, and the relationship of stem cell distribution to cancer origin are included. Aim 3 is based on the identification of prognostic markers by immunohistochemistry. Studies on the relation of caveolin-1 and thymosin beta-15 to tumor volume and histologic grade as well as studies on the degree to which sampling errors can affect the detection and accuracy of immunohistochemical abnormalities are included. The ultimate goal of these studies is the development of a thorough understanding of the early natural history of prostate cancer that can provide clinicians with better tools for the management of patients with this increasingly common disease.